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首页> 外文期刊>Spectrochimica acta, Part A. Molecular and biomolecular spectroscopy >Rapid analysis of soluble solid content in navel orange based on visible-near infrared spectroscopy combined with a swarm intelligence optimization method
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Rapid analysis of soluble solid content in navel orange based on visible-near infrared spectroscopy combined with a swarm intelligence optimization method

机译：基于可见近红外光谱的脐橙迅速分析脐橙和群智能优化方法

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摘要

Navel orange is a very popular fruit which is rich in nutrition necessary to human health. Nowadays, rapid, nondestructive and pollution-free analysis of internal organic compounds of fruit is an important and promising technology. The purpose of this paper is to present a swarm intelligence optimization method to extract the feature information of visible-near infrared (Vis-NIR) spectra of navel orange for rapid and nondestructive analysis of soluble solid content (SSC) in navel orange. This method was developed on particle swarm optimization (PSO) and named as piecewise particle swarm optimization (PPSO). The experimental results showed that the PPSO algorithm proposed in this paper overcame the disadvantage of PSO's premature convergence. The PLS model based on variables selected by PPSO for nondestructively detecting SSC of navel orange yield promising results, as the standard deviation of prediction (SEP) was 0.427 degrees Brix while the standard error of laboratory (SEL) was 0.22 degrees Brix. It indicated that the application of near infrared spectroscopy (NIRS) technology combined with PPSO for rapid analysis of soluble solid content in navel orange was feasible. (C) 2019 Elsevier B.V. All rights reserved.
机译：肚脐橙是一种非常流行的水果，其富于人类健康所必需的营养。如今，对内部有机化合物的速度快速，无损和无污染分析是一种重要和有前途的技术。本文的目的是提出一种群体智能优化方法，以提取脐橙的可见近红外（Vis-NIR）光谱的特征信息，用于脐橙可溶性固体含量（SSC）的快速和非破坏性分析。该方法是在粒子群优化（PSO）上开发的，并命名为分段粒子群优化（PPSO）。实验结果表明，本文提出的PPSO算法克服了PSO早产的缺点。基于PPSO选择的变量的PLS模型用于非破坏性检测脐橙产量的SSC，随着预测（SEP）的标准偏差为0.427度Brix，而实验室（SEL）的标准误差为0.22度Brix。它表明，近红外光谱（NIRS）技术的应用结合PPSO在脐橙中的可溶性固体含量快速分析是可行的。 （c）2019 Elsevier B.v.保留所有权利。

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